From “Physical Expansion” to “Human Development”: Regional IP Strong Chain, Deep Synergy of Investment in Physical and Human Capital, and Energy Green Controllability
「物理的拡大」から「人間開発」へ:地域IP強鎖、物的資本と人的資本への投資の深い相乗効果、およびエネルギーのグリーン制御可能性 (AI 翻訳)
Yi Wang, Luyan Zhou, Kun Lv
🤖 gxceed AI 要約
日本語
本論文は、中国の知的財産強省パイロットプログラムを自然実験として、地域のIPシステムと物的・人的資本投資の相乗効果がエネルギーのグリーン制御可能性に与える影響を分析。空間DIDとダブル機械学習を用いた結果、IP強鎖と深い相乗効果はともにグリーン制御可能性を向上させ、人的資本投資経路が物的資本よりも重要であることが示された。
English
This paper uses China's IP-strong province pilot as a quasi-natural experiment to examine the joint impact of regional IP systems and deep synergy between physical and human capital investment on energy green controllability. Using spatial DID and double machine learning, it finds that both the IP strong chain and deep synergy significantly improve green controllability, with the human capital investment path being more efficient than the physical capital path.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本研究は中国の経験に基づくが、日本でもIP戦略とグリーン成長の連携が議論されており、人材投資がグリーン移行の鍵となる点は示唆に富む。SSBJや移行計画の策定において、人的資本の役割を再考する契機となる。
In the global GX context
This Chinese policy study provides empirical evidence on how intellectual property regimes and factor allocation (especially human capital) drive energy transition. For global audiences, it highlights the importance of institutional design and human capital in achieving green controllability, complementing TCFD/ISSB frameworks that focus on governance and strategy.
👥 読者別の含意
🔬研究者:The causal chain from IP policy to energy green controllability via human capital offers a novel analytical framework for energy transition studies.
🏛政策担当者:Policymakers should consider the synergy between IP protection and human capital investment as a structural driver for green transition.
📄 Abstract(原文)
The fundamental dilemma of energy transition lies in whether an economy can guide its energy system to break free from deep dependence on fossil fuels in a sustained and orderly manner. This requires not only institutional incentives for innovation but also, more critically, a social-level shift in focus from “physical expansion” to “human development.” This paper incorporates these two conditions into a unified causal framework. Taking the pilot program for the construction of IP-strong provinces in China launched in 2016 as a quasi-natural experiment, and using panel data from 30 provincial-level administrative regions in China over the period 2010–2022, this study employs the Spatial Durbin Difference-in-Differences (SDM-DID) model and the Double Machine Learning (DML) method to examine the joint impacts and transmission mechanisms of the regional IP strong chain and the deep synergy between investment in physical capital and investment in human capital on energy green controllability. The findings are as follows. First, both the IP strong chain and deep synergy significantly improve energy green controllability. The local effect of deep synergy is far greater than the direct effect of the IP system itself, making it the core structural force driving the green transition. Second, the institutional dividend of the IP strong chain generates positive spatial spillovers to neighboring regions through the patent information disclosure channel. In contrast, the spatial spillovers of deep synergy are obstructed by administrative barriers and fiscal boundaries. Third, deep synergy plays a significant partial mediating role in the process through which the IP strong chain affects energy green controllability, with more than one-third of the total policy effect being released through this channel. Fourth, a path-wise test reveals a notable structural difference: the human capital investment path significantly outperforms the physical capital investment path in terms of transmission efficiency and robustness. This indicates that, at the current stage, the institutional effectiveness of the IP system in driving the green transition is largely achieved by improving the quality, capacity, and security level of human capital, rather than by restructuring the physical capital stock. The above conclusions remain robust after replacing the machine learning algorithm, adjusting the sample split ratio, and excluding the interference of concurrent competitive policies. This paper reveals the complete causal chain through which institutional public goods are transmitted to system governance capacity via the factor allocation structure, providing new empirical evidence for understanding the deep-seated relationship between intellectual property governance and the energy transition.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19143267first seen 2026-07-13 06:23:18
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